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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

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Showing 271–280 of 691 papers

TitleStatusHype
DScribe: Library of Descriptors for Machine Learning in Materials ScienceCode0
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and ReconstructionCode0
BayesGrad: Explaining Predictions of Graph Convolutional NetworksCode0
Domain-Adversarial Multi-Task Framework for Novel Therapeutic Property Prediction of CompoundsCode0
Do Graph Neural Networks Work for High Entropy Alloys?Code0
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptorsCode0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?Code0
Learning Hierarchical Interaction for Accurate Molecular Property PredictionCode0
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